
'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
UpperCamelCase_        =							logging.get_logger(__name__)

UpperCamelCase_        =							{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
    """self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
    """self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
    """self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
    """self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
    """fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
    """fc2""": """encoder.layers.*.feed_forward.output_dense""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
UpperCamelCase_        =							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def 						_UpperCAmelCase					(	_lowerCamelCase    :							Tuple		)     ->    List[str]:
  _lowerCAmelCase							:	Tuple 					=       {}
  with open(snake_case__	,   """r"""		) as file:
    for line_number, line in enumerate(snake_case__		):
      _lowerCAmelCase							:	Tuple 					=       line.strip()
      if line:
        _lowerCAmelCase							:	Optional[Any] 					=       line.split()
        _lowerCAmelCase							:	Optional[int] 					=       line_number
        _lowerCAmelCase							:	Dict 					=       words[0]
        _lowerCAmelCase							:	List[Any] 					=       value
  return result


def 						_UpperCAmelCase					(	_lowerCamelCase    :							int	,   _lowerCamelCase    :							List[Any]	,   _lowerCamelCase    :							Dict	,   _lowerCamelCase    :							int	,   _lowerCamelCase    :							List[str]		)     ->    Optional[Any]:
  for attribute in key.split("""."""		):
    _lowerCAmelCase							:	Optional[Any] 					=       getattr(snake_case__	,   snake_case__		)

  _lowerCAmelCase							:	List[Any] 					=       None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__		):
      _lowerCAmelCase							:	Optional[Any] 					=       PARAM_MAPPING[full_name.split("""."""		)[-1]]
      _lowerCAmelCase							:	List[Any] 					=       """param"""

  if weight_type is not None and weight_type != "param":
    _lowerCAmelCase							:	List[str] 					=       getattr(snake_case__	,   snake_case__		).shape
  elif weight_type is not None and weight_type == "param":
    _lowerCAmelCase							:	str 					=       hf_pointer
    for attribute in hf_param_name.split("""."""		):
      _lowerCAmelCase							:	List[Any] 					=       getattr(snake_case__	,   snake_case__		)
    _lowerCAmelCase							:	List[str] 					=       shape_pointer.shape

    # let's reduce dimension
    _lowerCAmelCase							:	str 					=       value[0]
  else:
    _lowerCAmelCase							:	Tuple 					=       hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'		)

  if weight_type == "weight":
    _lowerCAmelCase							:	Dict 					=       value
  elif weight_type == "weight_g":
    _lowerCAmelCase							:	List[str] 					=       value
  elif weight_type == "weight_v":
    _lowerCAmelCase							:	Tuple 					=       value
  elif weight_type == "bias":
    _lowerCAmelCase							:	int 					=       value
  elif weight_type == "param":
    for attribute in hf_param_name.split("""."""		):
      _lowerCAmelCase							:	Optional[int] 					=       getattr(snake_case__	,   snake_case__		)
    _lowerCAmelCase							:	Optional[Any] 					=       value
  else:
    _lowerCAmelCase							:	Any 					=       value

  logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'		)


def 						_UpperCAmelCase					(	_lowerCamelCase    :							str	,   _lowerCamelCase    :							Tuple	,   _lowerCamelCase    :							str	,   _lowerCamelCase    :							List[Any]	,   _lowerCamelCase    :							Any		)     ->    int:
  _lowerCAmelCase							:	Optional[int] 					=       None
  for param_key in PARAM_MAPPING.keys():
    if full_name.endswith(snake_case__		):
      _lowerCAmelCase							:	List[str] 					=       PARAM_MAPPING[full_name.split("""."""		)[-1]]
      _lowerCAmelCase							:	Tuple 					=       """param"""

  if weight_type is not None and weight_type != "param":
    _lowerCAmelCase							:	str 					=       """.""".join([key, weight_type]		)
  elif weight_type is not None and weight_type == "param":
    _lowerCAmelCase							:	Union[str, Any] 					=       """.""".join([key, hf_param_name]		)
  else:
    _lowerCAmelCase							:	Dict 					=       key

  _lowerCAmelCase							:	int 					=       value if """lm_head""" in full_key else value[0]


UpperCamelCase_        =							{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}


def 						_UpperCAmelCase					(	_lowerCamelCase    :							List[Any]	,   _lowerCamelCase    :							Dict	,   _lowerCamelCase    :							Tuple=None	,   _lowerCamelCase    :							Union[str, Any]=None		)     ->    Dict:
  _lowerCAmelCase							:	Optional[int] 					=       False
  for key, mapped_key in MAPPING.items():
    _lowerCAmelCase							:	Any 					=       """wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
    if key in name or key.split("""w2v_model."""		)[-1] == name.split("""."""		)[0]:
      _lowerCAmelCase							:	Tuple 					=       True
      if "*" in mapped_key:
        _lowerCAmelCase							:	int 					=       name.split(snake_case__		)[0].split("""."""		)[-2]
        _lowerCAmelCase							:	Optional[int] 					=       mapped_key.replace("""*"""	,   snake_case__		)
      if "weight_g" in name:
        _lowerCAmelCase							:	str 					=       """weight_g"""
      elif "weight_v" in name:
        _lowerCAmelCase							:	Dict 					=       """weight_v"""
      elif "bias" in name:
        _lowerCAmelCase							:	List[Any] 					=       """bias"""
      elif "weight" in name:
        # TODO: don't match quantizer.weight_proj
        _lowerCAmelCase							:	Optional[int] 					=       """weight"""
      else:
        _lowerCAmelCase							:	Union[str, Any] 					=       None
      if hf_dict is not None:
        rename_dict(snake_case__	,   snake_case__	,   snake_case__	,   snake_case__	,   snake_case__		)
      else:
        set_recursively(snake_case__	,   snake_case__	,   snake_case__	,   snake_case__	,   snake_case__		)
      return is_used
  return is_used


def 						_UpperCAmelCase					(	_lowerCamelCase    :							Dict	,   _lowerCamelCase    :							Optional[Any]	,   _lowerCamelCase    :							List[Any]		)     ->    Optional[Any]:
  _lowerCAmelCase							:	Tuple 					=       []
  _lowerCAmelCase							:	Dict 					=       fairseq_model.state_dict()

  _lowerCAmelCase							:	Optional[int] 					=       hf_model.wavaveca.feature_extractor

  for name, value in fairseq_dict.items():
    _lowerCAmelCase							:	str 					=       False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case__	,   snake_case__	,   snake_case__	,   snake_case__	,   hf_model.config.feat_extract_norm == """group"""	,   )
      _lowerCAmelCase							:	str 					=       True
    else:
      _lowerCAmelCase							:	Tuple 					=       load_wavaveca_layer(snake_case__	,   snake_case__	,   snake_case__		)
    if not is_used:
      unused_weights.append(snake_case__		)

  logger.warning(f'Unused weights: {unused_weights}'		)


def 						_UpperCAmelCase					(	_lowerCamelCase    :							List[Any]	,   _lowerCamelCase    :							Any	,   _lowerCamelCase    :							Optional[int]	,   _lowerCamelCase    :							int	,   _lowerCamelCase    :							Union[str, Any]		)     ->    Optional[Any]:
  _lowerCAmelCase							:	List[Any] 					=       full_name.split("""conv_layers."""		)[-1]
  _lowerCAmelCase							:	Any 					=       name.split("""."""		)
  _lowerCAmelCase							:	Optional[Any] 					=       int(items[0]		)
  _lowerCAmelCase							:	Tuple 					=       int(items[1]		)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'		)
      _lowerCAmelCase							:	Any 					=       value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'		)
      _lowerCAmelCase							:	List[str] 					=       value
      logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'		)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'		)
      _lowerCAmelCase							:	str 					=       value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f'{full_name} has size {value.shape}, but'
            f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'		)
      _lowerCAmelCase							:	str 					=       value
      logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
  else:
    unused_weights.append(snake_case__		)


@torch.no_grad()
def 						_UpperCAmelCase					(	_lowerCamelCase    :							List[Any]	,   _lowerCamelCase    :							Optional[int]	,   _lowerCamelCase    :							List[Any]=None	,   _lowerCamelCase    :							str=None	,   _lowerCamelCase    :							Optional[int]=True	,   _lowerCamelCase    :							Tuple=False		)     ->    Any:
  if config_path is not None:
    _lowerCAmelCase							:	List[Any] 					=       WavaVecaConfig.from_pretrained(snake_case__		)
  else:
    _lowerCAmelCase							:	str 					=       WavaVecaConfig()

  if is_seq_class:
    _lowerCAmelCase							:	int 					=       read_txt_into_dict(snake_case__		)
    _lowerCAmelCase							:	str 					=       idalabel
    _lowerCAmelCase							:	Tuple 					=       WavaVecaForSequenceClassification(snake_case__		)
    _lowerCAmelCase							:	Any 					=       WavaVecaFeatureExtractor(
        feature_size=1	,   sampling_rate=1_60_00	,   padding_value=0	,   do_normalize=snake_case__	,   return_attention_mask=snake_case__	,   )
    feature_extractor.save_pretrained(snake_case__		)

  elif is_finetuned:
    if dict_path:
      _lowerCAmelCase							:	Any 					=       Dictionary.load(snake_case__		)

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      _lowerCAmelCase							:	int 					=       target_dict.pad_index
      _lowerCAmelCase							:	Optional[Any] 					=       target_dict.bos_index
      _lowerCAmelCase							:	List[Any] 					=       target_dict.eos_index
      _lowerCAmelCase							:	Union[str, Any] 					=       len(target_dict.symbols		)
      _lowerCAmelCase							:	Any 					=       os.path.join(snake_case__	,   """vocab.json"""		)
      if not os.path.isdir(snake_case__		):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case__		)		)
        return
      os.makedirs(snake_case__	,   exist_ok=snake_case__		)
      _lowerCAmelCase							:	Any 					=       target_dict.indices

      # fairseq has the <pad> and <s> switched
      _lowerCAmelCase							:	Optional[Any] 					=       0
      _lowerCAmelCase							:	Dict 					=       1
      with open(snake_case__	,   """w"""	,   encoding="""utf-8"""		) as vocab_handle:
        json.dump(snake_case__	,   snake_case__		)
      _lowerCAmelCase							:	List[Any] 					=       WavaVecaCTCTokenizer(
          snake_case__	,   unk_token=target_dict.unk_word	,   pad_token=target_dict.pad_word	,   bos_token=target_dict.bos_word	,   eos_token=target_dict.eos_word	,   word_delimiter_token="""|"""	,   do_lower_case=snake_case__	,   )
      _lowerCAmelCase							:	Optional[Any] 					=       True if config.feat_extract_norm == """layer""" else False
      _lowerCAmelCase							:	List[str] 					=       WavaVecaFeatureExtractor(
          feature_size=1	,   sampling_rate=1_60_00	,   padding_value=0	,   do_normalize=snake_case__	,   return_attention_mask=snake_case__	,   )
      _lowerCAmelCase							:	Optional[int] 					=       WavaVecaProcessor(feature_extractor=snake_case__	,   tokenizer=snake_case__		)
      processor.save_pretrained(snake_case__		)

    _lowerCAmelCase							:	Any 					=       WavaVecaForCTC(snake_case__		)
  else:
    _lowerCAmelCase							:	Optional[int] 					=       WavaVecaForPreTraining(snake_case__		)

  if is_finetuned or is_seq_class:
    _lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase							:	Tuple 					=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""		)[:-1]		)}		)
  else:
    _lowerCAmelCase							:	Tuple 					=       argparse.Namespace(task="""audio_pretraining"""		)
    _lowerCAmelCase							:	List[str] 					=       fairseq.tasks.setup_task(snake_case__		)

    _lowerCAmelCase					,       _lowerCAmelCase					,       _lowerCAmelCase							:	Any 					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,   task=snake_case__		)

  _lowerCAmelCase							:	Optional[Any] 					=       model[0].eval()

  recursively_load_weights(snake_case__	,   snake_case__	,   not is_finetuned		)

  hf_wavavec.save_pretrained(snake_case__		)


if __name__ == "__main__":
     UpperCamelCase_        =							argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
     )
     parser.add_argument(
         """--is_seq_class""",
         action="""store_true""",
         help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
     )
     UpperCamelCase_        =							parser.parse_args()

     UpperCamelCase_        =							not args.not_finetuned and not args.is_seq_class
     convert_wavaveca_checkpoint(
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.dict_path,
         is_finetuned,
         args.is_seq_class,
     )








"""simple docstring"""




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase				:     Any					=				logging.get_logger(__name__)

lowerCAmelCase				:     int					=				"""▁"""

lowerCAmelCase				:     str					=				{"""vocab_file""": """prophetnet.tokenizer"""}

lowerCAmelCase				:     Union[str, Any]					=				{
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

lowerCAmelCase				:     Any					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

lowerCAmelCase				:     List[Any]					=				{
    """microsoft/xprophetnet-large-wiki100-cased""": 512,
}



def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   collections.OrderedDict()
			with open(snake_case__			,					"""r"""			,					encoding="""utf-8"""							) as reader:
						lowerCamelCase      =   reader.readlines()
			for index, token in enumerate(snake_case__							):
						lowerCamelCase      =   token.rstrip("""\n"""							)
						lowerCamelCase      =   index
			return vocab






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				__UpperCamelCase         =		VOCAB_FILES_NAMES
				__UpperCamelCase         =		PRETRAINED_VOCAB_FILES_MAP
				__UpperCamelCase         =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__UpperCamelCase         =		["input_ids", "attention_mask"]





				def __init__(			self						,					_a						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[SEP]"						,					_a="[UNK]"						,					_a="[PAD]"						,					_a="[CLS]"						,					_a="[MASK]"						,					_a = None						,					**_a						,					):





							"""simple docstring"""
							lowerCamelCase      =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=_a						,					eos_token=_a						,					sep_token=_a						,					unk_token=_a						,					pad_token=_a						,					cls_token=_a						,					mask_token=_a						,					sp_model_kwargs=self.sp_model_kwargs						,					**_a						,					)

							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(str(_a					)					)
							lowerCamelCase      =   vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# put special tokens and [unused] tokens into the vocab
							lowerCamelCase      =   {"""[PAD]""": 0, """[CLS]""": 1, """[SEP]""": 2, """[UNK]""": 3, """[MASK]""": 4}

							for i in range(10					):
										lowerCamelCase      =   f'[unused{i}]'
										lowerCamelCase      =   5 + i

							# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
							lowerCamelCase      =   12
							lowerCamelCase      =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}
							for k in self.fairseq_tokens_to_ids.keys():
										self.unique_no_split_tokens.append(_a					)





				def __getstate__(			self					):





							"""simple docstring"""
							lowerCamelCase      =   self.__dict__.copy()
							lowerCamelCase      =   None
							return state





				def __setstate__(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   d
							try:
										import sentencepiece as spm
							except ImportError:
										logger.warning(
										    """You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece"""
										    """ pip install sentencepiece"""					)
										raise

							# for backward compatibility
							if not hasattr(self						,					"""sp_model_kwargs"""					):
										lowerCamelCase      =   {}

							lowerCamelCase      =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
							self.sp_model.Load(self.vocab_file					)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None						,					_a = False					):





							"""simple docstring"""

							if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=_a						,					token_ids_a=_a						,					already_has_special_tokens=_a					)

							if token_ids_a is None:
										return ([0] * len(_a					)) + [1]
							return ([0] * len(_a					)) + [1] + ([0] * len(_a					)) + [1]





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							lowerCamelCase      =   [self.sep_token_id]

							if token_ids_a is None:
										return len(token_ids_a + sep					) * [0]
							return len(token_ids_a + sep + sep + token_ids_a + sep					) * [0]





				@property
				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							return len(self.sp_model					) + self.fairseq_offset





				def   _lowerCAmelCase							(			self					):





							"""simple docstring"""
							lowerCamelCase      =   {self.convert_ids_to_tokens(_a					): i for i in range(self.vocab_size					)}
							vocab.update(self.added_tokens_encoder					)
							return vocab





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							return self.sp_model.encode(_a						,					out_type=_a					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if token in self.fairseq_tokens_to_ids:
										return self.fairseq_tokens_to_ids[token]
							lowerCamelCase      =   self.sp_model.PieceToId(_a					)

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							if index in self.fairseq_ids_to_tokens:
										return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset					)





				def   _lowerCAmelCase							(			self						,					_a					):





							"""simple docstring"""
							lowerCamelCase      =   """""".join(_a					).replace(_a						,					""" """					).strip()
							return out_string





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""
							if not os.path.isdir(_a					):
										logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
										return
							lowerCamelCase      =   os.path.join(
							    _a						,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)

							if os.path.abspath(self.vocab_file					) != os.path.abspath(_a					) and os.path.isfile(self.vocab_file					):
										copyfile(self.vocab_file						,					_a					)
							elif not os.path.isfile(self.vocab_file					):
										with open(_a						,					"""wb"""					) as fi:
													lowerCamelCase      =   self.sp_model.serialized_model_proto()
													fi.write(_a					)

							return (out_vocab_file,)





				def   _lowerCAmelCase							(			self						,					_a						,					_a = None					):





							"""simple docstring"""

							if token_ids_a is None:
										return token_ids_a + [self.sep_token_id]
							lowerCamelCase      =   [self.sep_token_id]
							return token_ids_a + sep + token_ids_a + sep



